SEVerA

Overview

SEVerA treats self-evolving-agent synthesis as a constrained learning problem with hard formal specifications and softer utility objectives. It asks how adaptation can remain ambitious without becoming semantically feral.

Why it matters

It matters because most self-evolving-agent work optimizes for empirical improvement first and asks about correctness later. SEVerA begins from the opposite end of the table.

Distinctive trait

Its distinctive trait is constraint-first self-evolution: agent synthesis is guided by hard formal requirements plus softer performance goals rather than unconstrained search alone.

Relationships

Read SEVerA with formal-methods-for-agent-harnesses, self-evolving-workflows, and safety-and-permissions. It is the formal-methods counterweight to more empiricist systems such as MetaAgent and MetaClaw.